A Model-Based Deconvolution Approach to Solve Fiber Crossing in Diffusion-Weighted MR Imaging

Dell’Acqua, Flavio; Rizzo, Giovanna; Scifo, Paola; Clarke, Richard; Scotti, Giuseppe; Fazio, Ferruccio

doi:10.1109/tbme.2006.888830

Cited by 176 publications

(248 citation statements)

References 29 publications

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“…These are particularly dangerous, as these peaks cannot be discerned from peaks caused by the underlying microstructure. The stability and the occurrence of spurious peaks may also be ameliorated by using the damped Richardson-Lucy deconvolution (Dell'Acqua et al, 2007, Parker et al, 2013.…”

Section: Discussionmentioning

confidence: 99%

“…The regularization, while very efficient in suppressing negative lobes, also leads to significant shift in the shape of the fODF (Sotiropoulos et al, 2012;Dell'Acqua et al, 2007). Since the Bingham fitting scheme was demonstrated using CSD as deconvolution method, this can lead to misleading metrics, concerning the ground truth.…”

Section: Discussionmentioning

confidence: 99%

“…These methods do not require any prior assumptions on the number of underlying fiber bundles. They include Q-Ball Imaging (Tuch, 2004, Barnett, 2009, Canales-Rodríguez et al, 2009, Tristán-Vega et al, 2009, Aganj et al, 2010 approximating the diffusion orientation density function (dODF) and Spherical Deconvolution (SD) (Tournier et al, 2004, Dell'Acqua et al, 2007, Kaden et al, 2007, Tournier et al, 2007 modeling the fiber orientation density function (fODF). The fODF represents the direction dependent density of fibers in every voxel and therefore is an angular spatial fiber density.…”

Section: Local Modelsmentioning

confidence: 99%

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Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

et al. 2014

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Diffusion MRI (dMRI) measurements are used for inferring the microstructural properties of white matter and to reconstruct fiber pathways. Very often voxels contain complex fiber configurations comprising multiple bundles, rendering the simple diffusion tensor model unsuitable. Multi-compartment models deliver a convenient parameterization of the underlying complex fiber architecture, but pose challenges for fitting and model selection. Spherical deconvolution, in contrast, very economically produces a fiber orientation density function (fODF) without any explicit model assumptions. Since, however, the fODF is represented by spherical harmonics, a direct interpretation of the model parameters is impossible. Based on the fact that the fODF can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle), we offer a solution that seeks to combine the advantages of both approaches: first the fiber configuration is modeled as fODF represented by spherical harmonics and then each of the peaks is parameterized separately in order to characterize the underlying bundle. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second order statistics of the fiber orientations, from which we derive metrics for the parametric quantification of fiber bundles. We propose meaningful relationships between these measures and the underlying microstructural properties. We focus on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. We compare these metrics to the conventionally used fractional anisotropy (FA) and show how they may help to increase the specificity of the characterization of microstructural properties. While metric relying on the first moments of the Bingham distributions provide relatively robust results, second-order metrics representing the peak spread are only meaningful, if the SNR is very high and no fiber crossings are present in the voxel.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Local Modelsmentioning

confidence: 99%

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Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

et al. 2014

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“…Un faisceau longitudinal supé-rieur II plus gros dans l'hémisphère droit que dans l'hémisphère gauche est donc associé avec un traitement visuospatial plus rapide des évènements survenant à gauche comparé à ceux survenant à droite. En conclusion le degré de dominance de l'hémisphère droit pour le traitement visuospatial est associé à la taille du faisceau longitudinal supérieur II dans de 20 participants sains avec une nouvelle technologie d'imagerie par réso-nance magnétique qui permet d'explorer les connexions cérébrales chez l'être vivant : la tractographie des déconvolu-tions sphériques (SD) calculées à partir de l'imagerie par résonance magnétique (IRM) pondérée par la diffusion (DWI) [6,7] (voir Encadré). En parallèle, nous avons mesuré le degré de dominance de l'hémisphère cérébral droit avec des tests neuropsychologiques comme la bissection de ligne et une expérience simple de détection de cibles dans les champs visuels gauche et droit.…”

Section: Vers Des Applications Thérapeutiques ?unclassified

L’étude des connexions cérébrales révèle les bases anatomiques de la dominance de l’hémisphère droit pour l’attention spatiale

Schotten

2012

Med Sci (Paris)

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“…Spherical deconvolution is one method that can resolve crossing fibers by estimating the so-called fiber orientation distribution (FOD), a continuous distribution representing the partial volume of the underlying fibers as a function of orientation (7)(8)(9)(10)(11)(12). A potential application for these methods is for performing voxel-based analyses, as in the recently proposed apparent fiber density measure (13).…”

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confidence: 99%

Reorientation of fiber orientation distributions using apodized point spread functions

Raffelt¹,

Tournier²,

Crozier³

et al. 2011

Magnetic Resonance in Med

109

105

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Using high angular resolution diffusion-weighted images, spherical deconvolution enables multiple white matter fiber populations to be resolved within a single voxel by computing the fiber orientation distribution (FOD). Higher order information provided by FODs could improve several methods for investigating population differences in white matter, including image registration, voxel-based analysis, atlas-based segmentation and labeling, and group average fiber tractography. All of these methods require spatial normalization of FODs. In this article, a novel method to reorient the FOD is presented, which is an important step required for FOD spatial normalization. The proposed method was assessed using both qualitative and quantitative experiments, with numerical simulations and in vivo human data. Results demonstrate that the proposed method improves FOD reorientation accuracy, removes undesired artefacts, and decreases computation time compared to a previous approach. The utility of the proposed method is illustrated by nonlinear FOD spatial normalization of 10 human subjects. Key words: fiber orientation distribution; reorientation; normalization; diffusion MRI Diffusion-weighted imaging provides a unique method to investigate the architecture of white matter in vivo. To investigate group differences in the diffusion properties of white matter, correspondence between images is required to ensure that the same anatomical structures are being compared. The most common method for obtaining correspondence involves transforming data from each individual subject into a common co-ordinate or template space (a process called spatial normalization). For the remainder of this text, we refer to spatial normalization as simply normalization.The diffusion of water within each imaging voxel is commonly modeled using a diffusion tensor (1). In recent years, normalization of diffusion tensor measures such as fractional anisotropy (FA) (2) has been widely used to investigate voxelwise differences between diseased and healthy populations (see Assaf and Pasternak (3) for a recent review). However, the popular rank-2 diffusion tensor is only capable of modeling a single fiber population within each voxel. This is a serious limitation given recent work suggesting that 90% of white matter voxels contain multiple fiber populations (4). Consequently, in the majority of white matter, population differences in diffusion tensor measures can be influenced not only by differences in so-called white matter "integrity", but also by partial volume effects within crossing fiber regions (5,6).Using high angular resolution diffusion-weighted imaging, higher order models enable crossing fibers to be resolved and, therefore, provide more information than the diffusion tensor at a subvoxel level. Spherical deconvolution is one method that can resolve crossing fibers by estimating the so-called fiber orientation distribution (FOD), a continuous distribution representing the partial volume of the underlying fibers as a function of orientation (7-12...

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A Model-Based Deconvolution Approach to Solve Fiber Crossing in Diffusion-Weighted MR Imaging

Cited by 176 publications

References 29 publications

Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

Beyond fractional anisotropy: Extraction of bundle-specific structural metrics from crossing fiber models

L’étude des connexions cérébrales révèle les bases anatomiques de la dominance de l’hémisphère droit pour l’attention spatiale

Reorientation of fiber orientation distributions using apodized point spread functions

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